birth

detection/tools/create_crowd_anno.py

@@ -0,0 +1,95 @@
+import argparse
+import os
+import pickle as pkl
+import numpy as np
+import random
+from PIL import Image
+import concurrent.futures
+import json
+import mmcv
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='Generate MMDetection Annotations for Crowdhuman-like dataset')
+    parser.add_argument('--dataset', help='dataset name', type=str)
+    parser.add_argument('--dataset-split', help='dataset split, e.g. train, val', type=str)
+
+    args = parser.parse_args()
+    return args.dataset, args.dataset_split
+
+def load_func(fpath):
+    assert os.path.exists(fpath)
+    with open(fpath, 'r') as fid:
+        lines = fid.readlines()
+    records = [json.loads(line.strip('\n')) for line in lines]
+    return records
+
+def decode_annotations(records, dataset_path):
+    rec_ids = list(range(len(records)))
+    img_list = []
+    ann_list = []
+    ann_id = 1
+    for idx, rec_id in enumerate(rec_ids):
+        img_id = records[rec_id]['ID']
+        img_url = dataset_path + 'Images/' + img_id + '.jpg'
+        assert os.path.exists(img_url)
+        im = Image.open(img_url)
+        im_w, im_h = im.width, im.height
+
+        gt_box = records[rec_id]['gtboxes']
+        gt_box_len = len(gt_box)
+        img_dict = dict(
+            file_name=img_id + '.jpg',
+            height=im_h,
+            width=im_w,
+            id=idx
+        )
+        img_list.append(img_dict)
+        for ii in range(gt_box_len):
+            each_data = gt_box[ii]
+            x, y, w, h = each_data['fbox']
+
+            if w <= 0 or h <= 0:
+                continue
+            # x1 = x; y1 = y; x2 = x + w; y2 = y + h
+
+            valid_bbox = [x, y, w, h]
+            if each_data['tag'] == 'person':
+                tag = 1
+            else:
+                tag = -2
+            if 'extra' in each_data:
+                if 'ignore' in each_data['extra']:
+                    if each_data['extra']['ignore'] != 0:
+                        tag = -2
+            ann_dict = dict(
+                area=w * h,
+                iscrowd=1 if tag == -2 else 0,
+                image_id=idx,
+                bbox=[x, y, w, h],
+                category_id=1,
+                id=ann_id,
+                # ignore=1 if tag == -2 else 1,
+            )
+            ann_id += 1
+            ann_list.append(ann_dict)
+    cate_list = [{'supercategory': 'none', 'id': 1, 'name': 'person'}]
+    json_dict = dict(
+        images=img_list,
+        annotations=ann_list,
+        categories=cate_list
+    )
+    return json_dict
+
+if __name__ == "__main__":
+    dataset_name, dataset_type = parse_args()
+    dataset_path = 'data/%s/' % dataset_name
+    ch_file_path = dataset_path + 'annotations/annotation_%s.odgt' % dataset_type
+    json_file_path = dataset_path + 'annotations/annotation_%s.json' % dataset_type
+
+    records = load_func(ch_file_path)
+    print("Loading Annotations Done")
+
+    json_dict = decode_annotations(records, dataset_path)
+
+    print("Parsing Bbox Number: %d" % len(json_dict['annotations']))
+    mmcv.dump(json_dict, json_file_path)

detection/tools/evaluate/__init__.py

@@ -0,0 +1,2 @@
+from .compute_APMR import compute_APMR
+from .compute_JI import compute_JI_with_ignore